Weekly Exercises #5

library(tidyverse)     # for data cleaning and plotting
library(gardenR)       # for Lisa's garden data
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(ggthemes)      # for more themes (including theme_map())
library(plotly)        # for the ggplotly() - basic interactivity
library(gganimate)     # for adding animation layers to ggplots
library(transformr)    # for "tweening" (gganimate)
library(gifski)        # need the library for creating gifs but don't need to load each time
library(shiny)         # for creating interactive apps
library(ggimage)
theme_set(theme_minimal())

# SNCF Train data
small_trains <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/small_trains.csv") 

# Lisa's garden data
data("garden_harvest")

# Lisa's Mallorca cycling data
mallorca_bike_day7 <- read_csv("https://www.dropbox.com/s/zc6jan4ltmjtvy0/mallorca_bike_day7.csv?dl=1") %>% 
  select(1:4, speed)

# Heather Lendway's Ironman 70.3 Pan Am championships Panama data
panama_swim <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_swim_20160131.csv")

panama_bike <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_bike_20160131.csv")

panama_run <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_run_20160131.csv")

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Warm-up exercises from tutorial

1. Choose 2 graphs you have created for ANY assignment in this class and add interactivity using the ggplotly() function.

plastics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-01-26/plastics.csv')

plastics_long <- plastics %>% 
  pivot_longer(cols = empty:grand_total,
               names_to = "plastic_type",
               values_to = "count")

interactive_plastic <- plastics %>% 
  filter(parent_company == "Grand Total",
         grand_total > 5000, country != empty) %>% 
  select(country, grand_total, parent_company, year, volunteers) %>% 
  ggplot() + 
  geom_point(aes(x = grand_total, y = country, color = volunteers)) + 
  labs(title = "Countries that produced the most plastic waste ( > 5000)",
       x = "Amount of plastic waste", y = "Country", caption = "Jennifer Huang")

ggplotly(interactive_plastic)

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2. Use animation to tell an interesting story with the small_trains dataset that contains data from the SNCF (National Society of French Railways). These are Tidy Tuesday data! Read more about it here.

small_trains %>% 
  filter(service != "NA") %>% 
  mutate(month = month(month, label = TRUE, abbr = TRUE)) %>% 
  ggplot(aes(x = journey_time_avg, 
             y = num_arriving_late, 
             colour = service,
             group = month)) +
  geom_point(size = 1L) +
  labs(title = "Monthly late train arrivals by service type",
       subtitle = "Month: {closest_state}",
       x = "Average journey time",
       y = "Number of trains arriving late",
       color = "Train service type") + 
  scale_color_viridis_d() +
  theme_minimal() + 
  transition_states(month,
                    transition_length = 2,
                    state_length = 1)

anim_save("monthly_late_arrival.gif")

knitr::include_graphics("monthly_late_arrival.gif")

• Interestingly, national trains seems to delay more often than international trains. But there are also more national trains than international trains.

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Garden data

3. In this exercise, you will create a stacked area plot that reveals itself over time (see the geom_area() examples here. You will look at cumulative harvest of tomato varieties over time. You should do the following:

• From the garden_harvest data, filter the data to the tomatoes and find the daily harvest in pounds for each variety.
  
• Then, for each variety, find the cumulative harvest in pounds.
  
• Use the data you just made to create a static cumulative harvest area plot, with the areas filled with different colors for each vegetable and arranged (HINT: fct_reorder()) from most to least harvested (most on the bottom).
  
• Add animation to reveal the plot over date.

I have started the code for you below. The complete() function creates a row for all unique date/variety combinations. If a variety is not harvested on one of the harvest dates in the dataset, it is filled with a value of 0.

tomato_variety_cum_harvest <- garden_harvest %>% 
  filter(vegetable == "tomatoes") %>% 
  group_by(date, variety) %>% 
  summarize(daily_harvest_lb = sum(weight)*0.00220462) %>% 
  ungroup() %>% 
  complete(variety, date, fill = list(daily_harvest_lb = 0)) %>% 
  group_by(variety) %>% 
  mutate(variety = str_to_title(variety),
         cum_harvest_variety = cumsum(daily_harvest_lb)) %>% 
  ggplot(aes(x = date,
             y = cum_harvest_variety)) +
  geom_area(aes(fill = fct_reorder(variety, cum_harvest_variety, max))) + 
  labs(title = "Cumulative harvest (in pounds) for each tomatoe variety",
       subtitle = "Variety: {frame_along}",
       x = "",
       y = "Harvest (lbs)",
       fill = "Tomato variety") +
  scale_fill_viridis_d() + 
  transition_reveal(date)

animate(tomato_variety_cum_harvest, duration = 20)
anim_save("tomatoe_variety_harvest.gif")

knitr::include_graphics("tomatoe_variety_harvest.gif")

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Maps, animation, and movement!

4. Map my mallorca_bike_day7 bike ride using animation! Requirements:

• Plot on a map using ggmap.
  
• Show “current” location with a red point.
• Show path up until the current point.
  
• Color the path according to elevation.
  
• Show the time in the subtitle.
  
• CHALLENGE: use the ggimage package and geom_image to add a bike image instead of a red point. You can use this image. See here for an example.
• Add something of your own! And comment on if you prefer this to the static map and why or why not.

mallorca_map <- get_stamenmap(
    bbox = c(left = 2.2637, bottom = 39.5137, right = 2.6209, top = 39.7006), 
    maptype = "terrain",
    zoom = 12)

mallorca_bike_day7 <- mallorca_bike_day7 %>% 
  mutate(bike = "https://raw.githubusercontent.com/llendway/animation_and_interactivity/master/bike.png")


ggmap(mallorca_map) +
  geom_path(data = mallorca_bike_day7, 
             aes(x = lon, y = lat, color = ele),
             size = 1.2) +
  geom_image(data = mallorca_bike_day7,
             aes(x = lon, 
                 y = lat, 
                 image = bike),
                 size = 0.1) + 
  annotate("point", x = 2.586255, y = 39.66033, color = "red") +
  annotate("text", x = 2.5705, y = 39.650, 
           color = "purple", 
           label = 
           "Starting and 
           Ending City: Palma") + 
  labs(title = "Lisa's Mallorca, Spain Biking Journey",
       subtitle = "Time: {frame_along}",
       x = "",
       y = "") +
  scale_fill_viridis_d() + 
  transition_reveal(time) +
  scale_color_viridis_c(option = "magma") +
  theme_map() +
  theme(legend.background = element_blank())

anim_save("lisa_bike_journey.gif")

knitr::include_graphics("lisa_bike_journey.gif")

• I prefer this than the static map because it’s more engaging for the viewer to see where the journey is going. I also marked the starting and ending city, Palma, so that the viewer can be more immersed in the journey.

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5. In this exercise, you get to meet my sister, Heather! She is a proud Mac grad, currently works as a Data Scientist at 3M where she uses R everyday, and for a few years (while still holding a full-time job) she was a pro triathlete. You are going to map one of her races. The data from each discipline of the Ironman 70.3 Pan Am championships, Panama is in a separate file - panama_swim, panama_bike, and panama_run. Create a similar map to the one you created with my cycling data. You will need to make some small changes: 1. combine the files (HINT: bind_rows(), 2. make the leading dot a different color depending on the event (for an extra challenge, make it a different image using `geom_image()!).

combined_panama <-bind_rows(panama_swim, panama_bike, panama_run) 

panama_map <- get_stamenmap(
    bbox = c(left = -79.5762, bottom = 8.9060, right = -79.4878, top = 8.9909), 
    maptype = "terrain",
    zoom = 14)

combined_panama <- combined_panama %>% 
  mutate(event_icons = case_when(
    event == "Bike" ~ "https://raw.githubusercontent.com/llendway/animation_and_interactivity/master/bike.png",
    event == "Swim" ~ "https://img.icons8.com/android/24/000000/swimming.png",
    event == "Run" ~ "https://img.icons8.com/material-sharp/24/000000/running.png"))

ggmap(panama_map) +
  geom_path(data = combined_panama, 
             aes(x = lon, y = lat, color = event),
             size = 1.2) +
  geom_image(data = combined_panama,
             aes(x = lon, 
                 y = lat, 
                 image = event_icons),
                 size = 0.1) + 
  labs(title = "Heather's Panama Triatholon Journey",
       subtitle = "Time: {frame_along}",
       x = "",
       y = "") +
  transition_reveal(time) +
  scale_color_viridis_d(option = "magma") +
  theme_map() +
  theme(legend.background = element_blank())

anim_save("triatholon_heather.gif")

knitr::include_graphics("triatholon_heather.gif")

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COVID-19 data

6. In this exercise, you are going to replicate many of the features in this visualization by Aitish Bhatia but include all US states. Requirements:

• Create a new variable that computes the number of new cases in the past week (HINT: use the lag() function you’ve used in a previous set of exercises). Replace missing values with 0’s using replace_na().
• Filter the data to omit rows where the cumulative case counts are less than 20.
• Create a static plot with cumulative cases on the x-axis and new cases in the past 7 days on the y-axis. Connect the points for each state over time. HINTS: use geom_path() and add a group aesthetic. Put the x and y axis on the log scale and make the tick labels look nice - scales::comma is one option. This plot will look pretty ugly as is.
• Animate the plot to reveal the pattern by date. Display the date as the subtitle. Add a leading point to each state’s line (geom_point()) and add the state name as a label (geom_text() - you should look at the check_overlap argument).
  
• Use the animate() function to have 200 frames in your animation and make it 30 seconds long.
• Comment on what you observe.

covid_state_newcases <- covid19 %>% 
  group_by(state) %>% 
  mutate(past_wk = lag(cases, 7, order_by = date)) %>%
  replace_na(list(past_wk = 0)) %>% 
  ungroup() %>% 
  filter(past_wk >= 20) %>% 
  mutate(new_case_past_wk = cases - past_wk,
         ordered_state = fct_reorder2(state, date, cases)) %>% 
  ggplot(aes(x = cases, y = new_case_past_wk, group = ordered_state)) + 
  geom_path(color = "blue",
            size = 0.5) + 
  geom_point(color = "purple",
             size = 0.7) +
  geom_text(aes(label = ordered_state),
            check_overlap = TRUE) +
  scale_y_log10(breaks = 
                  scales::trans_breaks(
                    "log10", function(x)10^x),
                labels = scales::comma) +
  scale_x_log10(breaks = 
                  scales::trans_breaks(
                    "log10", function(x)10^x),
                labels = scales::comma) + 
  transition_reveal(date) + 
  labs(title = "Date: {frame_along}",
       x = "Total cases",
       y = "New cases in the past week") + 
  theme(legend.background = element_blank())

animate(covid_state_newcases,
        nframes = 200,
        duration = 30)
anim_save("covid_state_newcases.gif")

knitr::include_graphics("covid_state_newcases.gif")

• CVID cases in states like CA, NY, WA, NJ where there’s a major city or have more population shot up extremely early compared to states like MO or OK.
• Virgin Islands and Norther Mariana Islands had a slower rise in COVID cases partly because they are small islands that are not as visited frequently like NY or CA. They also shut down their state for a while too.
• Across all the states, however, there is an overall increasing trend for the rise of COVID cases despite that there has been decreases in case numbers here and there.

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7. In this exercise you will animate a map of the US, showing how cumulative COVID-19 cases per 10,000 residents has changed over time. This is similar to exercises 11 & 12 from the previous exercises, with the added animation! So, in the end, you should have something like the static map you made there, but animated over all the days. The code below gives the population estimates for each state and loads the states_map data. Here is a list of details you should include in the plot:

• Put date in the subtitle.
  
• Because there are so many dates, you are going to only do the animation for all Fridays. So, use wday() to create a day of week variable and filter to all the Fridays.
  
• Use the animate() function to make the animation 200 frames instead of the default 100 and to pause for 10 frames on the end frame.
  
• Use group = date in aes().
  
• Comment on what you see.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

states_map <- map_data("state")

covid_with_2018pop <-
  covid19 %>% 
  mutate(state = str_to_lower(state),
         weekday = wday(date, label = TRUE)) %>% 
  filter(weekday == "Fri") %>% 
  left_join(census_pop_est_2018,
            by = c("state")) %>% 
  mutate(covid_per_10000 = (cases/est_pop_2018)*10000) %>% 
  ggplot(aes(fill = covid_per_10000,
             group = date)) +
  geom_map(map = states_map,
           aes(map_id = state)) +
  scale_fill_viridis_c() +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  labs(title = "Most recent COVID cases per 10,000 people in US",
       subtitle = "Date: {closest_state}",
       fill = "Cases per 10,000 people") +
  transition_states(date, transition_length = 0) +
  theme_map() + 
  theme(legend.background = element_blank(),
        legend.position = "left",
        plot.title = element_text(face = "bold", hjust = 0.5))

animate(covid_with_2018pop,
        nframes = 200,
        end_pause = 10)
anim_save("covid_state_2018pop.gif")

knitr::include_graphics("covid_state_2018pop.gif")

• Across all US states, around March is when there’s 250 cases per 10,000 people. The map shows a faster color change from 750 cases per 10,000 people starting in October. After October, many more states lit up for 1,250 cases per 10,000 people.

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Your first shiny app (for next week!)

NOT DUE THIS WEEK! If any of you want to work ahead, this will be on next week’s exercises.

8. This app will also use the COVID data. Make sure you load that data and all the libraries you need in the app.R file you create. Below, you will post a link to the app that you publish on shinyapps.io. You will create an app to compare states’ cumulative number of COVID cases over time. The x-axis will be number of days since 20+ cases and the y-axis will be cumulative cases on the log scale (scale_y_log10()). We use number of days since 20+ cases on the x-axis so we can make better comparisons of the curve trajectories. You will have an input box where the user can choose which states to compare (selectInput()) and have a submit button to click once the user has chosen all states they’re interested in comparing. The graph should display a different line for each state, with labels either on the graph or in a legend. Color can be used if needed.

GitHub link

Link to Jennifer’s Weekly Exercise 5 GitHub Page